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Identifying the relationship between area characteristics by social class and bank branches distribution using GIS - A case study of Tokyo 23 Wards
3. Methodology
The primary study was conducted in the Tokyo 23 wards, Japan. The research was conducted in two stages:
In the first stage, market segmentation was undertaken by factor analysis and cluster analysis using socioeconomic variables related to social level. We used 2000 census data, NSC2001, and 2000 high-income taxpayer list for socioeconomic data.
In the second stage, the distribution characteristic of existing branches became evident in overlay analysis with result of segmentation. We used point data of branches of five major banks made by address matching processing and base map of Tokyo 23 wards divided into 3058 towns. We used SPSS11.5 and JMP5.0 for statistical analysis and ArcGIS8.0 for spatial analysis to identify the relationship between area characteristics by social class and distribution characteristic of existing bank branches in Tokyo23 wards.
4. Data Analysis and Results
Market segmentation by social class
- Factor Analysis
Variables : population, density, age(20’s, 30’s, 40’s, 50’s, 60’s), gender, households,
residence types (aged-households, married couple, have a children, single)
ownership (home ownership, rent a house)
education, job, employment,
income, high-tax payer, consumer purchasing power
The principle components analysis was applied to the dataset to extract from the series of 38 variables a set of factors capable of capturing the main features of the responses. Prior to the extraction of factors the Bartlett test of sphericity and the KMO measure of sampling adequacy confirmed that there was sufficient correlation among the variables to warrant the application of factor analysis. In order to simplify the factor patterns a VARIMAX rotation was conducted. In factor analysis, with a full set of 38 variables attributing to 9 factors, are defined as follows; Factor 1(purchasing power), Factor2 (social position), Factor3 (residence type), Factor 4 (the rich), Factor 5 (the elderly), Factor 6 (thirty-something), Factor 7 (the fifties vs. the twenties), Factor 8 (employment and ownership), Factor 9 (the forties)
- Cluster Analysis
Hierarchical cluster analysis using the Ward method was applied to the dataset. Visual inspection of the dendrograms revealed a jump in error viability measures between two and nine clusters and determined to nine groups(clusters). Each clusters was described as follows: Group1 (the elderly & life style stability), Group4 (30’s & life style stability), Group5 (the noble class), Group6 (instability of life), Group7 (lifestyle stability), Group8 (spending power), Group9 (wealth). Groups 2 and 3 are removed from this result because of outliers. Fig1 shows the number of towns contained by each clusters of 23 wards. Setagayaku, which is located in the west of Tokyo (Fig 1.) is the wealthiest area in Tokyo because, 65.8% of the towns in Setagayaku contained to Group 5 (High Class).
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